Results 241 to 250 of about 156,082 (279)

Relational Fuzzy Clustering with Multiple Kernels

2011 IEEE 11th International Conference on Data Mining Workshops, 2011
In this paper, the relational fuzzy c-means clustering algorithm is extended to an adaptive cluster model which maps data points to a high dimensional feature space through an optimal convex combination of homogenous kernels with respect to each cluster. This generalized model, called Relational Fuzzy C-Means with Multiple Kernels (RFCM-MK), strives to
Naouel Baili, Hichem Frigui
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Incremental fuzzy clustering with multiple kernels

2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2014
This paper presents two incremental clustering algorithms based on FCMK, a fuzzy clustering with multiple kernels algorithm we developed earlier [1]. The FCMK algorithm has a memory requirement of O(N2), where N is the number of objects in the data set.
Naouel Baili, Hichem Frigui
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Multiple Kernel Clustering With Compressed Subspace Alignment

IEEE Transactions on Neural Networks and Learning Systems, 2023
Multiple kernel clustering (MKC) has recently achieved remarkable progress in fusing multisource information to boost the clustering performance. However, the O(n2) memory consumption and O(n3) computational complexity prohibit these methods from being applied into median- or large-scale applications, where n denotes the number of samples.
Sihang Zhou 0001, Qiyuan Ou, Xinwang Liu 0002, Siqi Wang 0001, Luyan Liu, Siwei Wang 0001, En Zhu, Jianping Yin, Xin Xu 0001   +8 more
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A modified kernel clustering method with multiple factors

Pattern Analysis and Applications, 2014
We propose a simple and effective method about kernel clustering. This method takes many factors about kernel clustering into account. These factors include the selection of the initial centers of kernels, the ways of how to compute widths of kernels and the distances between patterns, different growing ways of kernels, and different kernel clustering ...
Changming Zhu, Daqi Gao
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A Novel Multiple Kernel Clustering Method

2012
Recently Multiple Kernel Learning (MKL) has gained increasing attention in constructing a combinational kernel from a number of basis kernels. In this paper, we proposed a novel approach of multiple kernel learning for clustering based on the kernel k-means algorithm. Rather than using a convex combination of multiple kernels over the whole input space,
Lujiang Zhang, Xiaohui Hu
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Multiple Kernel Learning Clustering with an Application to Malware

2012 IEEE 12th International Conference on Data Mining, 2012
With the increasing prevalence of richer, more complex data sources, learning with multiple views is becoming more widespread. Multiple kernel learning (MKL) has been developed to address this problem, but in general, the solutions provided by traditional MKL are restricted to a classification objective function.
Blake Anderson, Curtis B. Storlie, Terran Lane   +2 more
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Ratio-Based Multiple Kernel Clustering

2014
Maximum margin clustering (MMC) approaches extend the large margin principle of SVM to unsupervised learning with considerable success. In this work, we utilize the ratio between the margin and the intra-cluster variance, to explicitly consider both the separation and the compactness of the clusters in the objective. Moreover, we employ multiple kernel
Grigorios Tzortzis, Aristidis Likas
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Fuzzy clustering with multiple kernels in feature space

2012 IEEE International Conference on Fuzzy Systems, 2012
While classical kernel-based clustering algorithms are based on a single kernel, in practice it is often desirable to base clustering on combination of multiple kernels. In [1], we considered a fuzzy c-means with multiple kernels in observation space (FCMK-OS) algorithm which constructs the kernel from a number of Gaussian kernels and learns a ...
Naouel Baili, Hichem Frigui
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Multiple-kernel combination fuzzy clustering for community detection

Soft Computing, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hu Lu, Yuqing Song 0001, Hui Wei 0001
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Multiple Kernel Learning Based on Cooperative Clustering

2014
In recent years, kernel methods with single kernel had been challenged by the big data because of its heterogeneousness. In order to exploit the advantages of kernel methods, multiple kernel learning was proposed several years ago. However, the time and space complexity of multiple kernel learning increases greatly due to great amount computation of ...
Haiyang Du, Chuanhuan Yin, Shaomin Mu
openaire   +1 more source